1. Field
At least one example embodiment relates to face recognition technology for recognizing a face appearing in an image.
2. Description of the Related Art
Face recognition technology is considered a convenient and competitive bio-recognition technology that may verify a target without contact with the target. Face recognition technology may be dissimilar to other recognition technologies, for example, fingerprint and iris recognition, that require a user to perform a certain motion or an action. Face recognition technology has been widely used in various application fields, for example, security systems, mobile authentication, and multimedia searches due to convenience and effectiveness of the face recognition technology. However, using face recognition technology to recognize a user according to an image of the user may be sensitive to a face pose and a facial expression of a user, an occlusion, a change in illumination, and the like.
In some cases, the ability of a facial recognition system to recognize a user's face in an image of the user may include a pattern classifier. The pattern classifier may identify a registered face and a non-registered face. The pattern classifier may be trained to identify user faces based on, for example, a method using a neural network model. The neural network model may be a model obtained by modeling a feature of a human nerve cell through a mathematical expression, and may classify an input pattern as a desired (and/or, alternatively, predetermined) group.
In some cases, to solve an issue that the input pattern is classified as a desired (and/or, alternatively, predetermined) group, the neural network model may use an algorithm imitating an ability of human learning. Based on the algorithm, the neural network model may generate a mapping between the input pattern and an output pattern, which may be expressed as a learning ability of the neural network model.